WO2022209167A1 - Système de prédiction de durée de vie de composant et système d'assistance à la maintenance - Google Patents

Système de prédiction de durée de vie de composant et système d'assistance à la maintenance Download PDF

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Publication number
WO2022209167A1
WO2022209167A1 PCT/JP2022/001621 JP2022001621W WO2022209167A1 WO 2022209167 A1 WO2022209167 A1 WO 2022209167A1 JP 2022001621 W JP2022001621 W JP 2022001621W WO 2022209167 A1 WO2022209167 A1 WO 2022209167A1
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Prior art keywords
work machine
industry
server device
information
aerial photograph
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PCT/JP2022/001621
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English (en)
Japanese (ja)
Inventor
崇 奥村
浩之 金野
聡志 猪瀬
茂行 櫻井
信一 奥
知弘 米田
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日立建機株式会社
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Application filed by 日立建機株式会社 filed Critical 日立建機株式会社
Priority to CN202280005771.2A priority Critical patent/CN115917565A/zh
Priority to US18/023,902 priority patent/US20230333549A1/en
Priority to KR1020237006894A priority patent/KR20230042363A/ko
Priority to JP2023510509A priority patent/JPWO2022209167A1/ja
Priority to EP22779399.9A priority patent/EP4318339A1/fr
Publication of WO2022209167A1 publication Critical patent/WO2022209167A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection

Definitions

  • the present invention relates to a parts life prediction system and a maintenance support system, and more particularly to a parts life prediction system and a maintenance support system for predicting the life of parts of a working machine.
  • Patent Document 1 based on the position information of the working machine, the load applied to the parts in the operating area is acquired from the server, and based on the operation history of the working machine, the operating area A supply parts production prediction system is known that predicts the life of each part and transmits warning information to a working machine that has reached the end of its life.
  • a parts life prediction system is a parts life prediction system that is configured to be able to communicate with a work machine and includes a server device that predicts the life of a part of the work machine, wherein the server device is configured to predict the life of a part of the work machine. obtain the operating information of the working machine including the information of the operating position of the working machine, and estimate the type of industry in which the working machine is used based on the operating information of the working machine and a pre-stored aerial photograph of the operating position of the working machine and predicting the service life of the parts of the work machine based on the estimated industry.
  • the type of industry in which the work machine is used is estimated based on the operating information of the work machine and the pre-stored aerial photograph of the operating position of the work machine, and based on the estimated type of industry, the work machine to predict the life of parts in
  • the prediction accuracy can be improved.
  • FIG. 1 is a schematic configuration diagram showing a component life prediction system of an embodiment
  • FIG. 4 is a flowchart showing control processing of the server device; It is a figure which shows an example of an operating daily report table. It is a figure which shows an example of a component replacement history table. It is a figure which shows an example of a customer information table. It is a figure which shows an example of the operation characteristic of the work machine in each industry.
  • FIG. 4 is a diagram showing an example of a message displayed on a user terminal;
  • FIG. 10 is a flowchart showing another control process of the server device; It is a figure which shows an example of the failure factor displayed on the user terminal. It is a figure which shows an example of the failure factor displayed on the user terminal. It is a schematic block diagram which shows the maintenance support system of embodiment.
  • the wear amount of the sprocket tooth tip in the rental industry is The amount of sprocket tooth tip wear tends to be the largest in the scrap industry. This is because there are more metal pieces in scrapyards than in other industries, so they get caught between the teeth and links of the sprockets of hydraulic excavators, and work machines run while biting the caught metal pieces. By doing so, it is thought that the progress of sprocket tooth tip wear is accelerated.
  • the present inventors focused on the fact that the degree of wear and failure factors of working machine parts vary depending on the type of industry, estimated the type of industry in which the working machine is used, and determined the operating environment and operation of the working machine in the estimated industry.
  • the present inventors have found that prediction accuracy can be improved by estimating the service life of parts in consideration of their characteristics, and have completed the present invention.
  • FIG. 1 is a schematic configuration diagram showing a component life prediction system according to an embodiment.
  • a parts life prediction system 1 includes a work machine 2, a work machine management server 3, an aerial photograph management server 4, a server device 10 for predicting the life of parts of the work machine 2, and a user terminal. 5.
  • the work machine 2 , the work machine management server 3 , the aerial photograph management server 4 , the server device 10 and the user terminal 5 are configured to be able to communicate with each other via the network 6 .
  • the work machine 2 is not limited to a hydraulic excavator, and may be a wheel loader, bulldozer, or the like.
  • the work machine 2 is not limited to a single unit, and may be a plurality of units. Each working machine 2 is assigned an identification number.
  • the working machine 2 has a communication section 21, a vehicle body controller 22, a storage section 23, and an in-vehicle sensor 24.
  • the communication unit 21 is, for example, a wireless device for connecting to the network 6, and transmits various data detected by the on-vehicle sensor 24 to the work machine management server 3, and periodically sends operation information of the work machine 2 (for example, (at a frequency of once a day) to the work machine management server 3, and the part replacement information of the work machine 2 is sent to the work machine management server 3.
  • the communication unit 21 also receives an update program for the vehicle body controller 22 transmitted from the work machine management server 3 . When transmitting each data detected by the in-vehicle sensor 24, operation information, and parts replacement information to the work machine management server 3, the communication unit 21 also transmits the identification number of the work machine 2 at the same time.
  • the vehicle body controller 22 includes, for example, a CPU (Central Processing Unit) that executes calculations, a ROM (Read Only Memory) as a secondary storage device that records programs for calculations, and storage of calculation progress and temporary control variables. It is composed of a microcomputer combined with a RAM (random access memory) as a temporary storage device that stores , and controls the entire work machine 2 by executing the stored program.
  • a CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM random access memory
  • the storage unit 23 stores a program for controlling the work machine 2, data detected by the in-vehicle sensor 24, and the like.
  • the in-vehicle sensor 24 is various sensors attached to the work machine 2 , detects various data related to the operation of the work machine 2 , and outputs the detected data to the vehicle body controller 22 .
  • the in-vehicle sensor 24 includes, for example, an IMU (Inertial Measurement Unit) that detects the inclination of the arm, boom, and bucket of the work machine 2, a turning angle sensor that detects the turning angle of the turning body of the work machine 2, and a traveling operation that detects A travel operation sensor and the like are included.
  • IMU Inertial Measurement Unit
  • the working machine 2 is equipped with a GPS (Global Positioning System) sensor 25 .
  • the GPS sensor 25 has an antenna for receiving signals from GPS satellites, and detects information on the operating position of the work machine 2 in the earth coordinate system based on the time difference between signals received from a plurality of GPS satellites.
  • the work machine management server 3 is installed in the head office, branch office, factory, or management center of the manufacturer of the work machine 2 , and periodically collects operation information, parts replacement information, etc. transmitted from the work machine 2 .
  • centralized management of The work machine management server 3 includes, for example, a CPU (Central Processing Unit) that executes calculations, a ROM (Read Only Memory) as a secondary storage device that stores programs for calculations, and storage of the progress of calculations and temporary It consists of a microcomputer combined with a RAM (Random Access Memory) as a temporary storage device for storing control variables, and each processing is performed by executing a stored program.
  • a CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • the work machine management server 3 has a communication unit 31, a daily operation report table 32, a parts replacement history table 33, and a customer information table 34.
  • the communication unit 31 is, for example, a wireless device for connecting to the network 6, receives operation information, parts replacement information, etc. transmitted from the work machine 2, and communicates with the work machine 2 in response to a request from the server device 10. For example, it transmits operating information to the server device 10 .
  • the daily operation report table 32 is created based on the operation information transmitted from the work machine 2. For example, as shown in FIG. Items such as “latitude” and “longitude” are stored.
  • the machine number is the identification number of the working machine 2 .
  • the working day is the day on which the working information transmitted from the work machine 2 is received.
  • the operating time, travel time, excavation time, turning time, and fuel consumption are calculated based on data detected by the vehicle-mounted sensor 24 of the work machine 2 .
  • the latitude and longitude are detected by the GPS sensor 25 of the working machine 2 .
  • “working day” “working date and time” may be used.
  • the parts replacement history table 33 is created based on the parts replacement information transmitted from the work machine 2.
  • the parts replacement history table 33 includes items such as "machine number”, “date of replacement”, “number of replacement parts", and "quantity”.
  • the customer information table 34 manages information on customers who are users of the work machine 2 . In the customer information table 34, for example, as shown in FIG. 5, "machine number” and "customer name” are described.
  • the aerial photograph management server 4 collects and manages aerial photographs from all over the world and provides them to the outside via the network 6.
  • the aerial photograph here is also called an aerial photograph, and is preferably updated periodically.
  • the aerial photograph management server 4 has a communication unit 41, an aerial photograph storage unit 42 that stores aerial photographs, and a map information storage unit 43 that stores map information. Note that the aerial photograph management server 4 may not have the map information storage unit 43 .
  • the aerial photograph management server 4 stores an aerial photograph corresponding to a certain latitude and longitude (for example, the operating position of the work machine 2) from the aerial photograph storage unit 42. It extracts and transmits to the work machine management server 3 or the server device 10 .
  • the user terminal 5 is, for example, a smart phone, a tablet terminal, a mobile phone, a PC (Personal Computer), etc., and is carried by the maintenance personnel or the owner of the work machine 2.
  • the user terminal 5 has a communication section 51, an information acquisition section 52 for acquiring information transmitted from the server device 10, and a display section 53 including a display for displaying the acquired information.
  • the server device 10 includes, for example, a CPU (Central Processing Unit) that executes calculations, a ROM (Read Only Memory) as a secondary storage device that stores programs for calculations, and storage of calculation progress and temporary control variables. It is composed of a microcomputer that is combined with a RAM (Random Access Memory) as a temporary storage device that stores , and performs processing such as calculation, prediction, and estimation by executing the stored program.
  • a CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • the server device 10 determines whether the work machine 2 will be used based on the operation information of the work machine 2 transmitted from the work machine management server 3 and the aerial photograph transmitted from the aerial photograph management server 4.
  • the industry is estimated, the service life of the parts of the working machine 2 is predicted based on the estimated industry, and the failure factors of the parts are estimated. Therefore, the server device 10 of this embodiment has a communication section 11 , an information acquisition section 12 , an industry type estimation section 13 , a parts life prediction section 14 and a failure factor estimation section 15 .
  • the communication unit 11 is, for example, a wireless device for connecting to the network 6, and receives, for example, operation information of the work machine 2 transmitted from the work machine management server 3 and aerial photographs transmitted from the aerial photograph management server 4. Also, each information from the server device 10 is transmitted to the user terminal 5 .
  • the information acquisition unit 12 acquires the operation information of the work machine 2 from the work machine management server 3 and the aerial photograph from the aerial photograph management server 4 via the communication unit 11, and stores the acquired information.
  • the business type estimation unit 13 estimates the business type in which the work machine 2 is used based on the operation information of the work machine 2 acquired by the information acquisition unit 12 and the aerial photograph of the operating position of the work machine 2 . Specifically, the business type estimation unit 13 first identifies the business type in which the work machine 2 was used in the past, and associates the identified business type with an aerial photograph of the operating position when the work machine 2 was used in the past. Create teacher data for learning, and build a model for industry estimation by performing machine learning with deep learning on the created teacher data. Next, the industry estimation unit 13 uses the built industry estimation model to estimate the industry in which the work machine 2 is used based on the operation information of the work machine 2 and the aerial photograph of the operating position of the work machine 2. do.
  • the industry estimation unit 13 first determines the past operation information of the work machine 2 acquired by the information acquisition unit 12, Extract the information (ie, latitude and longitude) of the operating position. Next, the industry estimation unit 13 acquires an aerial photograph corresponding to the extracted longitude and latitude (that is, an aerial photograph of the operating position) from the aerial photograph management server 4 via the information acquisition unit 12 and the communication unit 11 .
  • the industry estimation unit 13 uses the acquired aerial photograph of the operating position of the work machine 2 and the industry in which the specified work machine 2 is used to generate training data for machine learning (i.e. A group of photo files) is created, and deep learning is applied to the created training data to automatically extract each feature, classify the industry, and build a model for industry estimation.
  • industries include scrap, raw material extraction such as quarrying and river sand, industrial waste, civil engineering work, demolition, road construction, steel and metal, forestry, port cargo handling, recycling, agriculture, and rental.
  • the business type estimation unit 13 of the present embodiment classifies the work machine 2 based on the acquired operation information of the work machine 2 and the operation characteristics of the work machine 2 in each business type created in advance, for example, by decision tree classification.
  • operation characteristics Estimate the industry in which the is used.
  • working machines tend to perform frequent operations (hereinafter referred to as "operation characteristics") depending on the type of industry (operation site).
  • the operating features are the features of how the work machine 2 is used in each industry.
  • the industry estimating unit 13 has the horizontal axis representing the industry (scrap, raw material extraction, industrial disposal, civil engineering work) and the vertical axis representing the operation characteristics (installation and operation of attachments, excavation, etc.). load, turning operation amount, non-operation time, and running frequency), and based on the operation characteristics of the work machine 2, the industry classification is performed by decision tree classification.
  • “ ⁇ " indicates that there are many legends
  • “ ⁇ ” indicates that there are normal legends
  • ⁇ indicates that there are few legends.
  • the "legend” refers to the operation of the work machine that is characteristic of the industry.
  • an example of excavation load is that industrial waste has a smaller excavation load than scrap, raw material extraction, and civil engineering work.
  • Information on attachment and operation of the attachment, excavation load, turning operation amount, non-operation time, and travel frequency can be obtained from the daily operation report of the work machine 2 .
  • Another feature of the operation of the work machine 2 in each industry is the difference in the movement distance of the work machine 2.
  • the operating range of the work machine is limited to a certain area, so the movement distance of the work machine is within about 100 m and the frequency of running is low.
  • the type of industry is port cargo handling, raw material extraction, civil engineering work, or road construction
  • the movement distance of the work machine is about 500 m or less.
  • the industry is quarrying, the distance traveled by the working machine will be greater, within about 1 km. .
  • the business type estimation unit 13 can classify the business type using the difference in the travel distance in each business type (in other words, the characteristics of the travel distance in each business type). Note that the travel distance of the work machine 2 is obtained from the daily operating report of the work machine 2 .
  • the business type estimation unit 13 performs machine learning on the aerial photograph using the result of business type estimation based on the operation information of the work machine 2 and the operation characteristics of the work machine 2 in each business type. Improve the accuracy of the industry estimation model built by That is, the industry estimation unit 13 combines the result of industry estimation based on the operation information of the work machine 2 and the operation characteristics of the work machine 2 in each industry, and the result of industry estimation by performing machine learning on the aerial photograph. In addition, it is possible to construct a model for industry estimation with high accuracy. By doing so, it is possible to further improve the accuracy of the industry classification.
  • the industry type estimation unit 13 sends an aerial photograph of the working position of the working machine 2 to the aerial photograph management server 4 based on the information of the working position of the working machine 2 . and based on the acquired aerial photograph and the model for estimating the business type constructed above, the business type in which the work machine 2 is used is estimated.
  • the industry estimating unit 13 further estimates the industry based on the operation information of the working machine 2 whose industry is to be estimated and the operating characteristics of the working machine 2 in each industry. It is preferable to improve the accuracy of industry estimation by combining the results of industry estimation based on the industry estimation model. In addition, it is more preferable that the estimation result of the business type estimation unit 13 is fed back to the teacher data for machine learning.
  • the part life prediction unit 14 predicts the life of the parts of the work machine 2 based on the business type estimated by the business type estimation unit 13 . Specifically, the part life prediction unit 14 predicts the operating time (total operating time) of the work machine 2 using graphs, tables, etc., which have been created in advance and show the relationship between the operating time and wear rate of model parts for each industry. Predict the life of model parts in that industry based on In this embodiment, the service life of a component means the remaining usage time of the component when it continues to operate under the current usage conditions. Note that the number of model parts for each industry may be one or more.
  • the part life prediction unit 14 refers to the part replacement history stored in the part replacement history table 33 of the work machine management server 3 to predict the life of the model part. For example, when a model part is replaced, the total run time for that part will be reset and recalculated.
  • the failure factor estimation unit 15 estimates failure factors based on the business type estimated by the business type estimation unit 13, taking into consideration the failure tendency of the work machine 2 in that business type.
  • the failure tendency of the work machine 2 for each industry is extracted by statistically processing past failure factors for each industry.
  • the failure factor estimator 15 considers the above-described failure tendency and determines the cause of pinching of metal pieces. Precisely estimate whether the failure is due to
  • the failure factor estimator 15 determines whether the cause is soil or sand entering the air cleaner. is estimated with emphasis on
  • the server device 10 transmits to the user terminal 5 the result estimated by the industry type estimation unit 13 , the result estimated by the part life estimation unit 14 , and the result estimated by the failure factor estimation unit 15 .
  • step S ⁇ b>11 the information acquisition unit 12 of the server device 10 acquires operation information of the work machine 2 from the work machine management server 3 via the communication unit 11 .
  • step S12 the information acquisition unit 12 extracts information (latitude and longitude) of the operating position of the working machine 2 from the acquired operating information of the working machine 2, and transmits the aerial photograph of the operating position to the communication unit 11. obtained from the aerial photograph management server 4 via the Internet and stored.
  • the operation information acquisition (step S11) and the aerial photograph acquisition (step S12) here are automatically performed at predetermined intervals by the information acquisition unit 12, for example.
  • step S13 the business type estimation unit 13 creates training data for machine learning based on the acquired operation information and aerial photographs, and builds a business type estimation model, as described above.
  • step S14 the server device 10 acquires the information (latitude and longitude) of the operating position of the work machine 2 whose life is to be predicted, and sends an aerial photograph from the aerial photograph management server 4 based on the latitude and longitude information. get.
  • the server device 10 may make a determination according to an instruction from the work machine management server 3, or may make a decision by itself.
  • step S15 following step S14 the business type estimating unit 13 selects the type of business in which the work machine 2 whose part life is to be predicted is used based on the acquired aerial photograph and the operation information of the work machine 2, as described above. presume. That is, industry estimation is performed using both the industry estimation based on the aerial photograph and the industry estimation model, and the industry estimation based on the operation information of the work machine 2 and the operation characteristics of the work machine 2 in each industry. .
  • step S16 the parts life prediction unit 14 predicts the life of the model parts for each industry based on the results of the industries estimated in step S15.
  • a model part is a part that serves as a benchmark (indicator) for life prediction.
  • the server device 10 extracts the model parts of the industry based on the result of the industry estimated in step S15. For example, if it is estimated in step S15 that the work machine 2 is used for scrap, the server device 10 extracts a sprocket as a scrap model part.
  • the part life prediction unit 14 predicts the life of the sprocket based on the operating time (total operating time) of the working machine 2 based on a relationship graph created in advance between the sprocket operating time and wear rate. Furthermore, the part life prediction unit 14 calculates the replacement timing of the sprocket based on the predicted life of the sprocket.
  • step S17 the server device 10 determines whether or not the model part replacement timing for each industry is equal to or greater than the threshold. For example, the server device 10 compares the sprocket replacement timing calculated by the part life prediction unit 14 with a preset threshold value, and determines whether or not the sprocket replacement timing is equal to or greater than the threshold value.
  • the threshold value here is a guideline for issuing an alarm to call attention, and is set as, for example, 90% of the replacement time. It should be noted that this threshold value may be appropriately changed according to the situation.
  • step S18 the server device 10 notifies the user terminal 5 of an alarm via the communication unit 11, and also transmits to the user terminal 5 an e-mail describing an address for confirming the cumulative damage of the part and the predicted value until replacement. do.
  • a message stating, "When operating under the current operating conditions, the service life threshold will be reached in 100 hours. Order parts.” etc.” is displayed on the display unit 53 .
  • Messages are not limited to text messages, but simple messages such as "A (replacement recommendation: high)", “B (replacement recommendation: medium)", “C (replacement recommendation: low)” in descending order of life threshold. It may be a display in the classified classification. The owner or the like of the work machine 2 can easily grasp the service life of the parts by looking at the displayed message, so that it becomes easier to plan the purchase of the parts.
  • the message is not limited to text display, and may be displayed, for example, by displaying an image of the work machine and blinking the corresponding portion.
  • blinking it is displayed in colors such as red, yellow, and blue in order of the life threshold value (when the replacement time is approaching), and by tapping the blinking part, further information (summary) about the failure is notified.
  • the faulty part can be arbitrarily enlarged or reduced by pinch-in/pinch-out operations on the operation screen of the user terminal. By doing so, the cumulative damage and life of the parts of the work machine 2 can be intuitively visually recognized.
  • the number of model parts for each industry may be one or more.
  • life prediction and replacement time of each model part are calculated in step S16, and the replacement time of each model part is compared with a threshold value in step S17. Then, if it is determined that one of the model parts has a replacement time equal to or greater than the threshold value, an alarm notification and an e-mail are sent to the user terminal 5 .
  • the information acquiring unit 12 acquires the operating information of the working machine 2 including the information of the operating position of the working machine 2 and the aerial photograph of the operating position of the working machine 2;
  • An industry estimation unit 13 that estimates the industry in which the work machine 2 is used by machine learning based on the operating information of the work machine 2 and the aerial photograph of the operating position, and predicts the life of the parts of the work machine 2 based on the estimated industry.
  • a component life prediction unit 14 for In this way, the operating environment and operating characteristics of the work machine 2 in each industry can be taken into account to predict the service life of the parts according to the industry, thereby improving the prediction accuracy.
  • control processing of the server device 10 of this embodiment is not limited to that described above, and for example, the control processing shown in FIG. 8 can be performed.
  • Steps S21 to S23 shown in FIG. 8 are the same as steps S11 to S13 described above, so redundant description will be omitted.
  • the failure factor estimator 15 statistically processes the past failure factors for each industry and extracts the failure tendency of the work machine 2 for each industry, as described above.
  • step S25 the server device 10 determines whether or not an abnormality is detected. At this time, the server device 10 determines whether or not there is an abnormality detected based on the detection result that is detected by the vehicle-mounted sensor 24 of the work machine 2 and transmitted via the communication unit 21 . This step S25 is repeatedly executed until an abnormality is detected.
  • step S26 the business type estimation unit 13 estimates the business type of the work machine 2 in which the abnormality is detected based on the acquired aerial photograph and the operation information of the work machine 2, as described above. Specifically, the information acquisition unit 12 of the server device 10 acquires information (latitude and longitude) of the operating position of the work machine 2 in which the abnormality has been detected, and acquires an aerial photograph of the operating position from the aerial photograph management server 4. do. Next, the industry estimating unit 13 performs the same processing as in step S15 described above to estimate the industry of the working machine 2 in which the abnormality has been detected.
  • step S27 following step S26 the failure factor estimation unit 15 uses the failure tendency of the work machine 2 for each industry extracted in step S24 to estimate the failure factor related to the abnormality detected in step S25.
  • step S28 following step S27 the server device 10 notifies the user terminal 5 via the communication unit 11 of an alarm to the effect that there is a possibility that the component is out of order, and sends an address to confirm the estimated cause of the failure. is also sent to the user terminal 5.
  • the contents of "failure alarm name”, "industry estimation” and “factor estimation” are displayed on the display section 53 as shown in FIGS. to be displayed.
  • the industry is presumed to be civil engineering work, and sand mixed in engine parts is presumed to be a possible cause of abnormal combustion failure.
  • the industry is estimated to be scrap, and it is estimated that there is a possibility that a metal piece is caught in an underbody part as a cause of failure of the running parts. Then, the owner or the like of the working machine 2 can easily grasp the cause of the failure by looking at the displayed contents, and can easily take measures such as countermeasures against the failure.
  • the display of failure factors is not limited to text display. That is, the content of the failure corresponding to the failure alarm may be displayed by displaying an image diagram showing the silhouette of the work machine and blinking the corresponding portion. At that time, as in the example of FIG. 7, by tapping the blinking part, information such as industry estimation is additionally displayed, but the probability may be displayed as a percentage. At that time, when the probability of industry estimation is similar for at least two industries (for example, civil engineering (45%), industrial waste (40%)), the industry with the highest probability and the industry with the next highest probability are listed together. be. The range of approximation may be set arbitrarily. Such notation allows the maintenance personnel of the working machine 2 to intuitively grasp the information about the failure. It is possible to prepare for
  • the maintenance support system 100 will be described below with reference to FIG.
  • the maintenance support system 100 differs from the component life prediction system 1 described above in the structure of the server device 10A. Since other structures are the same as those of the parts life prediction system 1, the same reference numerals as those of the parts life prediction system 1 are given, and redundant explanations are omitted.
  • the maintenance support system 100 is a system for supporting maintenance work by maintenance personnel and the like.
  • a server device 10A configured to be communicable with an aerial photograph management server 4 that stores aerial photographs based on position information is included.
  • the work machine management server 3 transmits the collected operation information of the work machine 2 to the server device 10A, and the server device 10A sends the operation information of the work machine 2 to the aerial photograph management server 4.
  • the aerial photograph management server 4 extracts an aerial photograph corresponding to the information on the working position included in the working information of the work machine 2 and sends it to the server device 10A.
  • the server device 10A estimates the type of industry in which the work machine 2 is used based on the operating information of the work machine 2 and the aerial photograph of the operating position of the work machine 2. Furthermore, the server device 10A estimates the type of industry in which the work machine 2 is used based on the operation information of the work machine 2 and the operation characteristics of the work machine 2 in each type of industry.
  • the server device 10A does not have the component life prediction unit 14 and the failure factor estimation unit 15, and has a communication unit 11, an information acquisition unit 12, and an industry type estimation unit. 13 only.
  • the server device 10A transmits the result of the business type estimated by the business type estimation unit 13 to the terminal of the maintenance worker (that is, the user terminal 5).
  • the server device 10A also transmit the prediction accuracy together with the estimated result of the business type.
  • a maintenance worker can see the transmitted result to understand the type of business in which the work machine 2 is used, and can bring a maintenance tool suitable for the type of business to the site. As a result, it is possible to prevent an increase in the burden on the maintenance personnel due to carrying unnecessary tools, and improve the efficiency of the maintenance work.
  • the maintenance support system 100 of the present embodiment similarly to the parts life prediction system 1 described above, it is possible to improve the prediction accuracy of predicting the life of the parts of the work machine 2, thereby improving the efficiency of maintenance work. becomes possible.

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Abstract

L'invention concerne un système de prédiction de durée de vie de composant (1) qui est configuré pour être apte à communiquer avec une machine de travail (2) et qui est pourvu d'un dispositif serveur (10) servant à prédire la durée de vie d'un composant de la machine de travail (2). Le dispositif serveur (10) : acquiert des informations de fonctionnement de la machine de travail (2), comprenant des informations d'une position de fonctionnement de la machine de travail (2), et une photographie aérienne pré-stockée montrant la position de fonctionnement de la machine de travail (2) ; déduit un type de travail pour lequel la machine de travail (2) est utilisée sur la base des informations de fonctionnement acquises de la machine de travail (2) et de la photographie aérienne acquise montrant la position de fonctionnement de la machine de travail (2) ; et prédit la durée de vie d'un composant de la machine de travail (2) sur la base du type de travail déduit.
PCT/JP2022/001621 2021-03-31 2022-01-18 Système de prédiction de durée de vie de composant et système d'assistance à la maintenance WO2022209167A1 (fr)

Priority Applications (5)

Application Number Priority Date Filing Date Title
CN202280005771.2A CN115917565A (zh) 2021-03-31 2022-01-18 部件寿命预测系统及维护支援系统
US18/023,902 US20230333549A1 (en) 2021-03-31 2022-01-18 Component Service Life Prediction System and Maintenance Assistance System
KR1020237006894A KR20230042363A (ko) 2021-03-31 2022-01-18 부품 수명 예측 시스템 및 보수 지원 시스템
JP2023510509A JPWO2022209167A1 (fr) 2021-03-31 2022-01-18
EP22779399.9A EP4318339A1 (fr) 2021-03-31 2022-01-18 Système de prédiction de durée de vie de composant et système d'assistance à la maintenance

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JP2021059112 2021-03-31
JP2021-059112 2021-03-31

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WO2022209167A1 true WO2022209167A1 (fr) 2022-10-06

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US (1) US20230333549A1 (fr)
EP (1) EP4318339A1 (fr)
JP (1) JPWO2022209167A1 (fr)
KR (1) KR20230042363A (fr)
CN (1) CN115917565A (fr)
WO (1) WO2022209167A1 (fr)

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Publication number Priority date Publication date Assignee Title
JP7380302B2 (ja) * 2020-02-18 2023-11-15 コベルコ建機株式会社 操作支援サーバ、操作支援システムおよび操作支援方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005173979A (ja) 2003-12-11 2005-06-30 Komatsu Ltd 補給部品生産予測システム、補給部品生産予測方法及びプログラム
JP2014153929A (ja) * 2013-02-08 2014-08-25 Hitachi Constr Mach Co Ltd 作業内容データベースの作成方法
WO2014132903A1 (fr) * 2013-02-26 2014-09-04 住友重機械工業株式会社 Dispositif d'assistance pour pelle et procédé d'assistance pour pelle
JP2017008524A (ja) * 2015-06-18 2017-01-12 日立建機株式会社 建設機械の交換品管理システム
JP2021059112A (ja) 2019-10-07 2021-04-15 日東電工株式会社 印刷層付フィルム積層体、該印刷層付フィルム積層体を含む光学積層体、およびこれらを用いた画像表示装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005173979A (ja) 2003-12-11 2005-06-30 Komatsu Ltd 補給部品生産予測システム、補給部品生産予測方法及びプログラム
JP2014153929A (ja) * 2013-02-08 2014-08-25 Hitachi Constr Mach Co Ltd 作業内容データベースの作成方法
WO2014132903A1 (fr) * 2013-02-26 2014-09-04 住友重機械工業株式会社 Dispositif d'assistance pour pelle et procédé d'assistance pour pelle
JP2017008524A (ja) * 2015-06-18 2017-01-12 日立建機株式会社 建設機械の交換品管理システム
JP2021059112A (ja) 2019-10-07 2021-04-15 日東電工株式会社 印刷層付フィルム積層体、該印刷層付フィルム積層体を含む光学積層体、およびこれらを用いた画像表示装置

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EP4318339A1 (fr) 2024-02-07
KR20230042363A (ko) 2023-03-28
US20230333549A1 (en) 2023-10-19
JPWO2022209167A1 (fr) 2022-10-06
CN115917565A (zh) 2023-04-04

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